Latency optimization has recently received significant attention in FL research. The work in [1]} proposed a joint device scheduling and bandwidth allocation framework for wireless FL to improve the convergence rate of ML model training. Another study in [2]} investigated semi-asynchronous FL, focusing on the convergence analysis of model training under edge heterogeneity and non-independent and identically distributed (non-IID) data distributions across the edge devices. An asynchronous FL scheme was considered in [3]} for unmanned aerial vehicles (UAVs)-assisted networks, where a joint optimization of device selection, UAVs placement and resource management was considered to minimize the model exchange latency and ML training loss via deep reinforcement learning (DRL). To mitigate straggler effects caused by resource-limited clients, the authors in [4]} presented a partial offloading-assisted FL scheme using game theory. A partial offloading-based FL solution was also proposed in [5]} for edge computing, focusing on the delay analysis of the data offloading and model update. Similarly, a convex optimization approach was applied in [6]} to minimize the energy consumption of model updating and sharing.
